Implementing and Optimizing SMA, EMA, SMMA, and LWMA in MQL5
Summary
The article explains how to calculate four common moving averages from price history in MQL5: simple, exponential, smoothed, and linearly weighted. It outlines each method’s weighting behavior and provides indicator implementation examples, starting with a straightforward SMA loop that averages a fixed window of closing prices.
It also explains why recalculating every historical bar on every tick is inefficient. The recommended indicator pattern uses the previous-calculation count to calculate the full series at initialization, then update only the bars that need new values. The examples show how to handle indicator buffers and price arrays, and compare the responsiveness and smoothness of the four averages. The article is an implementation guide rather than a trading-system evaluation: it presents no performance tests or evidence that any moving-average type produces profitable signals, and the best choice depends on the strategy and market conditions.
Key ideas
- An SMA gives equal weight to every closing price in its selected window.
- EMA, SMMA, and LWMA apply different weighting schemes, changing how quickly the average responds to price movements.
- A nested loop that recomputes every historical average on each tick can make an indicator unnecessarily slow.
- Using the previous-calculation count allows an indicator to reuse completed values and update only new or changed bars.
- Moving averages can smooth price noise and help describe trends, but the article does not establish trading profitability.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.